{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Linear Discriminant Analysis Classification"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# yahoo finance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-05-26T13:20:17.019Z",
          "iopub.execute_input": "2022-05-26T13:20:17.024Z",
          "iopub.status.idle": "2022-05-26T13:20:18.660Z",
          "shell.execute_reply": "2022-05-26T13:20:18.680Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2019-01-01'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume\nDate                                                    \n2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02</th>\n      <td>3.85</td>\n      <td>3.98</td>\n      <td>3.84</td>\n      <td>3.95</td>\n      <td>3.95</td>\n      <td>20548400</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-05-26T13:20:18.668Z",
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        }
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    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Open_Close'] = (dataset['Open'] - dataset['Adj Close'])/dataset['Open']\n",
        "dataset['High_Low'] = (dataset['High'] - dataset['Low'])/dataset['Low']\n",
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume  Open_Close  \\\nDate                                                                   \n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200   -0.005025   \n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300   -0.029925   \n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100    0.002387   \n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700    0.011820   \n2014-01-09  4.20  4.23  4.05   4.09       4.09  30667600    0.026190   \n\n            High_Low  Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns  \nDate                                                                           \n2014-01-03  0.030928                  1                 1         1  0.012658  \n2014-01-06  0.047619                  1                 1         1  0.032500  \n2014-01-07  0.034063                  0                 1         0  0.012106  \n2014-01-08  0.028986                  0                 0         0  0.000000  \n2014-01-09  0.044444                  0                 0         1 -0.021531  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Open_Close</th>\n      <th>High_Low</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Returns</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n      <td>-0.005025</td>\n      <td>0.030928</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.012658</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n      <td>-0.029925</td>\n      <td>0.047619</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.032500</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n      <td>0.002387</td>\n      <td>0.034063</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.012106</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n      <td>0.011820</td>\n      <td>0.028986</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2014-01-09</th>\n      <td>4.20</td>\n      <td>4.23</td>\n      <td>4.05</td>\n      <td>4.09</td>\n      <td>4.09</td>\n      <td>30667600</td>\n      <td>0.026190</td>\n      <td>0.044444</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.021531</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
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        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-05-26T13:20:21.074Z",
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          "shell.execute_reply": "2022-05-26T13:20:21.142Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset[['Open', 'High', 'Low', 'Volume', 'Adj Close','Returns']].values\n",
        "y = dataset['Buy_Sell'].values"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-05-26T13:20:21.097Z",
          "iopub.execute_input": "2022-05-26T13:20:21.101Z",
          "iopub.status.idle": "2022-05-26T13:20:21.108Z",
          "shell.execute_reply": "2022-05-26T13:20:21.145Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-05-26T13:20:21.114Z",
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        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearDiscriminantAnalysis()\n",
        "\n",
        "sss = StratifiedShuffleSplit(n_splits=5, test_size=0.50, random_state=None)\n",
        "sss.get_n_splits(X, y)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "5"
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-05-26T13:20:24.913Z",
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          "iopub.status.idle": "2022-05-26T13:20:24.927Z",
          "shell.execute_reply": "2022-05-26T13:20:24.965Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm_sum = np.zeros((2,2))"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-05-26T13:20:24.934Z",
          "iopub.execute_input": "2022-05-26T13:20:24.938Z",
          "iopub.status.idle": "2022-05-26T13:20:24.943Z",
          "shell.execute_reply": "2022-05-26T13:20:24.969Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for train_index, test_index in sss.split(X, y):\n",
        "    X_train, X_test = X[train_index], X[test_index]\n",
        "    y_train, y_test = y[train_index], y[test_index]\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "    cm = confusion_matrix(y_test, y_pred)\n",
        "    cm_sum = cm_sum + cm"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-05-26T13:20:24.950Z",
          "iopub.execute_input": "2022-05-26T13:20:24.954Z",
          "shell.execute_reply": "2022-05-26T13:20:24.972Z",
          "iopub.status.idle": "2022-05-26T13:20:24.977Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('\\nLinear Discriminant Analysis Algorithms')\n",
        "print('\\nConfusion Matrix')\n",
        "print('_'*20)\n",
        "print('     Predicted')\n",
        "print('     pos neg')\n",
        "print('pos: %i %i' % (cm_sum[1,1], cm_sum[0,1]))\n",
        "print('neg: %i %i' % (cm_sum[1,1], cm_sum[0,1]))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Linear Discriminant Analysis Algorithms\n",
            "\n",
            "Confusion Matrix\n",
            "____________________\n",
            "     Predicted\n",
            "     pos neg\n",
            "pos: 640 661\n",
            "neg: 640 661\n"
          ]
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-05-26T13:20:24.984Z",
          "iopub.execute_input": "2022-05-26T13:20:24.988Z",
          "iopub.status.idle": "2022-05-26T13:20:24.997Z",
          "shell.execute_reply": "2022-05-26T13:20:25.023Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "print('Accuracy Score: ', accuracy_score(y_test, y_pred))\n",
        "print('Accuracy Score Normalized: ',accuracy_score(y_test, y_pred, normalize=False))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score:  0.505564387917329\n",
            "Accuracy Score Normalized:  318\n"
          ]
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-05-26T13:20:25.002Z",
          "iopub.execute_input": "2022-05-26T13:20:25.005Z",
          "iopub.status.idle": "2022-05-26T13:20:25.014Z",
          "shell.execute_reply": "2022-05-26T13:20:25.026Z"
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      "name": "python3"
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      "name": "python",
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      "codemirror_mode": {
        "name": "ipython",
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      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
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    "kernelspec": {
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    "nteract": {
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